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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Unsupervised Classification of Atrial Electrograms for Electroanatomic Mapping of Human Persistent Atrial Fibrillation

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Author(s):
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Almeida, Tiago P. [1, 2, 3] ; Soriano, Diogo C. [4] ; Mase, Michela [5, 6] ; Ravelli, Flavia [7] ; Bezerra, Arthur S. [8] ; Li, Xin [9, 3] ; Chu, Gavin S. [9, 10] ; Salinet, Joao [4] ; Stafford, Peter J. [10] ; Ng, G. Andre [9, 10, 11] ; Schlindwein, Fernando S. [3, 11] ; Yoneyama, Takashi [8]
Total Authors: 12
Affiliation:
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[1] Aeronaut Inst Technol, BR-12228900 Sao Jose Dos Campos, SP - Brazil
[2] Univ Leicester, Glenfield Hosp, Dept Cardiovasc Sci, Leicester LE3 90P, Leics - England
[3] Univ Leicester, Sch Engn, Leicester LE1 7RH, Leics - England
[4] Fed Univ ABC, Santo Andre, SP - Brazil
[5] Univ Trento, Bruno Kessler Fdn FBK, Healthcare Res & Innovat Program, IRCS HTA, Trento, TN - Italy
[6] Eurac Res, Inst Mt Emergency Med, Bolzano - Italy
[7] Univ Trento, Trento, TN - Italy
[8] Aeronaut Inst Technol, Tokyo - Japan
[9] Univ Leicester, Dept Cardiovasc Sci, Leicester, Leics - England
[10] Univ Hosp Leicester NHS Trust, Leicester, Leics - England
[11] Glenfield Hosp, Leicester Cardiovasc Biomed Res Ctr, Natl Inst Hlth Res, Leicester, Leics - England
Total Affiliations: 11
Document type: Journal article
Source: IEEE Transactions on Biomedical Engineering; v. 68, n. 4, p. 1131-1141, APR 2021.
Web of Science Citations: 1
Abstract

Objective: Ablation treatment for persistent atrial fibrillation (persAF) remains challenging due to the absence of a `ground truth' for atrial substrate characterization and the presence of multiple mechanisms driving the arrhythmia. We implemented an unsupervised classification to identify clusters of atrial electrograms (AEGs) with similar patterns, which were then validated by AEG-derived markers. Methods: 956 bipolar AEGs were collected from 11 persAF patients. CARTO variables (Biosense Webster; ICL, ACI and SCI) were used to create a 3D space, and subsequently used to perform an unsupervised classification with k-means. The characteristics of the identified groups were investigated using nine AEG-derived markers: sample entropy (SampEn), dominant frequency, organization index (OI), determinism, laminarity, recurrence rate (RR), peak-to-peak (PP) amplitude, cycle length (CL), and wave similarity (WS). Results: Five AEG classes with distinct characteristics were identified (F = 582, P<0.0001). The presence of fractionation increased from class 1 to 5, as reflected by the nine markers. Class 1 (25%) included organized AEGs with high WS, determinism, laminarity, and RR, and low SampEn. Class 5 (20%) comprised fractionated AEGs with in low WS, OI, determinism, laminarity, and RR, and in high SampEn. Classes 2 (12%), 3 (13%) and 4 (30%) suggested different degrees of AEG organization. Conclusions: Our results expand and reinterpret the criteria used for automated AEG classification. The nine markers highlighted electrophysiological differences among the five classes found by the k-means, which could provide a more complete characterization of persAF substrate during ablation target identification in future clinical studies. (AU)

FAPESP's process: 19/05192-1 - Multivariate classification of atrial substrate during atrial fibrillation
Grantee:Arthur Santos Bezerra
Support type: Scholarships in Brazil - Master
FAPESP's process: 15/12799-9 - 42th annual scientific meeting of Computing in Cardiology
Grantee:João Loures Salinet Júnior
Support type: Research Grants - Meeting - Abroad
FAPESP's process: 18/02251-4 - Characterizing persistent atrial fibrillation dynamics using computational models and recurrence quantification analysis - towards novel biomarkers for guiding therapy
Grantee:Tiago Paggi de Almeida
Support type: Scholarships abroad - Research Internship - Post-doctor
FAPESP's process: 17/00319-8 - Atrial substrate identification in patients with chronic atrial fibrillation using multivariate statistical models and multiple attributes from atrial electrograms.
Grantee:Tiago Paggi de Almeida
Support type: Scholarships in Brazil - Post-Doctorate